Detection of conditions by analysis of gases or vapors

ABSTRACT

The invention discloses a method for monitoring at least one condition in a patient comprising the steps of (a) obtaining samples from the patient over a period of time; (b) flowing the samples, or gases associated with or produced by the samples, over at least one gas sensor; c) measuring the response or responses of the at least one gas sensor; and (d) correlating the response or responses with the occurrence or state of the at least one condition. A method for identifying a micro-organism comprising the steps of (a) providing at least one gas sensor; (b) compiling a database of responses to at least one known micro-organism under a variety of culturing conditions; c) abstracting gas or vapor from a detection region and flowing the same over the at least one gas sensor and observing the response of the sensor or sensors; and (d) comparing the response to the database.

This invention relates to the detection of conditions, in particular tothe detection of conditions in a patient and the detection ofmicroorganisms by analysis of gases or vapors emitted therefrom.

It is known that many gaseous or volatile species can be detected andidentified by so-called “electronic noses”, which are usually devicescomprising an array of individual gas sensing elements. For example, thepresent applicants produce such an instrument having up to thirty twogas sensing elements, each element having a different semiconductingpolymer. The semiconducting polymers typically display broad andoverlapping responses towards gaseous species. However, this is turnedto advantage when an array of such polymers is employed. On exposure toa gas or volatile species, the resistances of the semiconducting organicpolymer vary—but to different extents. Thus the pattern of resistancevariation across the array of sensors is indicative of the species beingdetected. Further information concerning polymers and the techniquesused to interrogate them may be found in International Publication No.WO 96/00384 and references therein.

International Publication No. WO 95/33848 describes a method fordetecting bacteria via detection of the characteristic vapors emanatingtherefrom as a result of bacterial metabolism by an array of gassensors, in particular an array of semiconducting organic polymers ofthe type described above. The technique is of potentially wide andsignificant use in the medical area. However, to date, methods andapparatus suitable for practical, day to day use in a clinicalenvironment have not been available. Furthermore, it would be desirableto detect and monitor a whole range of clinical conditions, which mightinclude bacterial infections, but which might also include otherconditions, such as viral or fungal infection.

The present invention addresses these problems and concerns.

According to a first aspect of the invention there is provided a methodfor monitoring at least one condition in a patient comprising the stepsof:

obtaining samples from the patient over a period of time;

flowing the samples, or gases and/or vapors associated with, or producedby the samples, over at least one gas sensor;

measuring the response or responses of the at least one gas sensor as afunction of time; and

correlating the response or responses with the occurrence or state ofthe at least one condition.

In this way, monitoring for the onset of a condition, or monitoring ofthe progression of a condition is possible, the data being obtained veryrapidly, since laborious and time consuming culturing steps are notrequired.

The samples may comprise respiratory gases.

The samples may comprise swabbed samples obtained from the patient.

The samples may comprise blood.

The condition monitored may be a disease state, and the progressionand/or regression of the disease state may be monitored.

The condition may be a bacterial infection.

The condition may be a viral, fungal or parasitic infection.

The response or responses may be correlated with the effectiveness of acourse of treatment.

The response or responses may be correlated with the progress of ahealing process.

The response or responses may be correlated with the occurrence or stateof the condition or conditions by a trained neural network.

An array of gas sensors may be employed. The pattern of responses of thesensors in the array may be correlated with the occurrence of or stateof at least one condition.

The samples may be obtained continuously from the patient. The samples,or gas and/or vapor associated with or produced by the samples, may becontinuously flowed over the at least one gas sensor. In this way,on-line monitoring of conditions by reference to gases and vapors ispossible.

The response or responses of the at least one gas sensor may be measuredcontinuously.

Alternatively, a plurality of measurements may be made over a period oftime.

The samples may comprise respiratory gases obtained from a ventilator.

The samples may comprise blood undergoing a dialysis treatment. Gasesproduced by a waste product containing solution may be measured by theat least one gas sensor. The removal of urea from the blood sample maybe monitored by measuring ammonia evolved from the waste productcontaining solution.

According to a second aspect of the invention there is provided a methodfor identifying a micro-organism comprising the steps of:

providing at least one gas sensor;

compiling a database of responses to at least one known micro-organismunder a variety of culturing conditions;

abstracting gas and/or vapor from a detection region and flowing thesame over said the at least one gas sensor and observing the response ofthe sensor or sensors; and

comparing the response to the database.

The database may comprise responses to at least one known bacterium.

The database may comprise responses to a plurality of different isolatesof a single bacterial species.

The mirco-organism may comprise a virus, fungus or parasite.

The database may comprise responses to at least one known micro-organismcultured under a variety of nutrient conditions.

The database may comprise responses to at least one known micro-organismcultured at a variety of temperatures.

The database may comprise responses to at least one known micro-organismobtained at different stages in the life cycle of the micro-organism.

The method may further identify at least one condition in a patient inwhich gas and/or vapor produced by the patient, or by a sample obtainedfrom the patient, is flowed over the at least one gas sensor. At least aportion of the database may be compiled from responses of at least onegas sensor to gas or vapor produced by a patient, or by a sampleobtained from the patient.

The database may comprise responses to at least one known micro-organismobtained at different stages during the course of treatment.

An array of gas sensors may be employed.

The compilation of the database may comprise training a neural network.In other words, the trained neural network is regarded as a “database”for the present purposes, and the pattern recognition processes employedby such networks are similarly regarded as “comparing the response tothe database”.

The response of the sensors may be used to provide information about thedetection region, such as the nutrient conditions, the nature of thesubstrate or the location of the detection region.

The gas sensor or sensors may comprise a gas sensitive material. Anelectrical property of the gas sensitive material may vary on exposureto gases and/or vapors.

The gas sensitive material may comprise semi-conducting polymer.

The gas sensor or sensors may comprise metal oxide semiconductor (MOS),quartz resonator or surface acoustic wave (SAW) devices.

Methods and apparatus in accordance with the invention will now bedescribed with reference to the accompanying drawings, in which:

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts schematically apparatus for performing an improved methodfor identifying bacteria;

FIG. 2 shows response as a function of time for patient one;

FIG. 3 shows response as a function of time for patient three;

FIG. 4 shows response as a function of time for patient ten;

FIG. 5 is a schematic diagram of apparatus for on-line monitoring ofrespiratory gases; and

FIG. 6 shows response as a function of time for two patient s presentingwith pulmonary candida albicans.

The invention comprises, in one aspect, a method for monitoring at leastone condition in a patient comprising the steps of:

obtaining samples from the patient over a period of time;

flowing the samples, or gases and/or vapors associated with or producedby the samples, over at least one gas sensor;

measuring the response or responses of the at least one gas sensor; and

correlating the response or responses with the occurrence or state ofthe at least one condition.

Although, as discussed above, it is known from International PatentPublication No. WO 95/33848 that bacteria can be identified by way ofdetecting characteristic gases, the application of this technique to aclinical environment has not been forthcoming. The present inventionenables monitoring of a patient over a period of time via the detectionof certain gases and vapors. The monitoring can be in order to detectthe onset of a condition, or to monitor the progression of a condition.The condition can be a bacterial infection, but the monitoring of a widerange of other conditions is also within the scope of the invention. Itshould be noted that the patient can be human, but the technique mightbe applied to the monitoring of conditions in animals.

International Publication No. WO 95/33848 only describes resultsobtained from cultured samples or bacteria. It is a considerableadvantage of the present invention that measurements can be performedmore or less directly upon samples obtained from the patient, since datais obtained very rapidly, with no need for laborious and time consumingculturing steps to be performed. It is surprising that such measurementscan be made successfully, since the samples are obtained from a complexenvironment of mixed flora and fauna. This makes it difficult todifferentiate gases produced due to a given condition from gasesproduced by other conditions and processes. In techniques in which aseparate culturing process is performed under defined nutrientconditions and temperatures, the production of certain micro-organismsover other micro-organisms which require different culturing conditionsis favoured. Therefore, the use of a culturing process inevitablyinvolves a pre-selection of the micro-organisms identifiable by anysubsequent detection method. The present invention does not involve sucha pre-selection, it being sufficiently sensitive to make measurements onsamples directly obtained from the patient, without requiring thepopulation enhancement occurring during culturing.

The samples can comprise respiratory gases, in which instance thepatient might exhale into a tube which communicates with the gas sensoror sensors, or into a bag which is sealed, the headspace in the bagbeing subsequently flowed across the sensor(s). Alternatively, samplesmight comprise swabbed samples obtained from the patient. The swabs canbe placed in a closed environment, so that a headspace is developedwhich contains gases and vapors produced from the swabbed samples. Theheadspace is then flowed across the sensor(s) in ways well known in theart. Alternatively still, the samples might comprise blood.

An important aspect of the invention is on-line monitoring of a patient.In this aspect, samples are obtained continuously from the patient.Usually, the samples, or gas and/or vapor associated with or produced bythe samples, are continuously flowed over the gas sensor(s). Theresponse(s) of the gas sensor(s) can be measured continuously—whichrepresents a totally “on-line” system—or it may be desirable to makemore occasional measurements, ie. a plurality of measurements may bemade over a period of time.

FIG. 5 shows apparatus for on-line monitoring of at least one conditionin a patient comprising:

at least one gas sensor 50;

sampling means 52 for continuously obtaining a sample from the patientand flowing the sample, or gases and/or vapors associated with orproduced by the sample, over the at least one gas sensor 50; and

measurement means 54 for measuring the response of the at least one gassensor as a function of time.

In the embodiment shown in FIG. 5, the sample comprises respiratorygases and the sampling means 52 is a ventilator. The respiratory gasesflow from the output 52 a of the ventilator 52. In general, an array 50of gas sensors is utilised although, as discussed more fully below, itis possible that a single gas sensor may suffice. The array 50 of gassensors is positioned in direct communication with the output 52 a ofthe ventilator, and thus continuously samples the respiratory gases ofthe patient. A computer 56 is employed to control the overall gassensing procedure, and for data analysis purposes.

The gas sensors preferably comprise a gas sensitive material, ie. anactive sensing medium, a property of which varies on exposure to theanalyte gas. In preferred embodiments, an electrical property of the gassensitive material varies on exposure to gases. The electrical propertymight be d.c. resistance, or an a.c. impedance property such asreactance or capacitance. It is also possible to monitor changes inoptical or spectroscopic properties.

In a particularly preferred form of gas sensor, the gas sensitivematerial comprises semiconducting polymers. Examples of semiconductingpolymers are polypyrrole and substituted derivatives thereof. Typically,gases are detected by measuring changes in the d.c. resistance of thepolymer (see for example Persaud K C, Bartlett J G and Pelosi P, in“Robots and Biological Systems: Towards a new bionics?”, Eds. Darios P,Sandini G and Aebisher P, NATO ASI Series F: Computer and SystemsSciences 102 (1993) 579). However, it is also possible to measure a.c.impedance properties, as taught by British Patent GB 2 203 553.Excellent results can be obtained if measurements of quantities relatedto the resonant frequency, such as dissipation factor, are performed, astaught by International Publication No. WO 97/19349. Alternative gassensors include metal oxide semiconductor (MOS), quartz resonator orsurface acoustic wave (SAW) devices. It is a feature of such gas sensorsthat they are not selective: an individual gas sensor is generallysensitive to a range of gases. One way of performing species selectiveidentification is to employ an array of gas sensors having differentresponse characteristics, and to observe the pattern of sensor responsesacross the array. The pattern of response represents a characteristic“fingerprint” for the measured gas. For the present purpose, an “array”should be considered as two or more gas sensors. In the case ofsemiconducting polymers in which measurements of d.c. resistances aremade, the array typically comprises ten or more gas sensors, preferablytwenty or more. However, the use of a.c. or like interrogation permitsresults in more information to be obtained from a single sensor, andthus fewer sensors are required in the array. It may be possible toemploy a single gas sensor.

Using an array of gas sensors, the pattern of responses of the sensorsin the array can be correlated with the occurrence of or state of thecondition or conditions. The pattern of response can be transformed into2 or 3 dimensional space by an appropriate transformation, such as aSalmon mapping algorithm. Such a transformation can be performed on thecomputer 56, and aids the identification of differences in patterns byhumans. This is particularly important if the correlation of theresponse pattern with the condition(s) is performed by human judgement.Alternatively, or additionally, a neural network might be employed toassess the response pattern and perform the correlation.

It is a considerable advantage that the present invention may be appliedto monitoring a wide range of conditions. The condition monitored can bea disease state. The purpose may be to detect the onset of such acondition, or to monitor the progression and/or regression of a diseasestate. The condition can be a bacterial, viral, fungal or parasiticinfection. Furthermore, it is possible, as demonstrated in the Examples,to correlate the response(s) of the sensor(s) with the effectiveness ofa course of treatment and/or the process of a healing process. Thecorrelation of response(s) with the occurrence of certain condition orconditions might be performed by a skilled person, but advantageously itis performed by suitably adapted software, such as a trained neuralnetwork.

Returning to the on-line monitoring of conditions through gas detection,another important embodiment involves the on-line monitoring ofdialysis. In the dialysis process, waste products diffuse across amembrane into a waste product containing solution and are therebyremoved from the blood sample. Direct monitoring of gases and/or vaporsassociated with the blood is complicated by the fact that the blood iscirculated in a closed system. Instead, it is preferable to detect gasesand/or vapors produced by the waste product containing solution. Inparticular, the removal of urea from the blood sample may be monitoredby measuring ammonia evolved from the waste product containing solution.Dialysis is complete when no further increases in ammonia concentrationare detected.

EXAMPLE 1

The progress of chronic venous ulcers was monitored using a twentysensor AromaScan A20S instrument in a confidential study.

Patients with chronic non-healing venous or mixed (venous and arterial)ulcers were studied. All had previously been treated by four-orthree-layer bandaging over several weeks and had failed to improve. Nonehad pure arterial disease (all Ankle Brachial Pressure Indices [ABPIs]were>0.6), diabetes mellitus, rheumatoid arthritis or other conditionsassociated with leg ulceration and poor wound healing. Each patientunderwent a biopsy prior to the study.

The ulcers were dressed weekly with dressing maintained in contact withthe ulcer surface by a four or three layer elastic bandage. Thedressings were non-adherent contact dressings (Johnson and Johnson‘N/A’). Dressings were removed with forceps and placed into 250 ml Duranbottles. Before introduction to the A20S instrument, the Duran bottlecap was replaced with one having inlet and outlet ports (see FIG. 1).The Duran bottle was heated to 37° C. for 30 minutes and the headspacepumped across the sensor array. In this manner, weekly measurements ofgases and vapors emanating from the contact dressings were made.

Eight men (mean age 69.6, range 52-81) and seven women (mean age 70.9,range 55-81) were entered into the study, which was conducted over a sixmonth period. None of these fifteen ulcers appeared to be clinicallyinfected.

Potentially pathogenic bacteria were cultured from thirteen out of thefifteen patients (see Table 1). Beta-haemolytic streptococci werecultured from the biopsies of eleven patients (often in mixed growthwith other organisms). Staphylococcus aureus and Peptostreptococcus sp.were each isolated from a single patient. Patient underwent anappropriate course of treatment.

In thirteen out of fifteen patients in the study, the responses obtainedwith the A20S instrument correlated with the clinical healing progressof the patients. FIGS. 2 to 4 show illustrative results. In theseFigures, responses are shown in the form of a Sammon map: each response,which comprises the outputs of the twenty individual sensors in thearray, is reduced to two dimensions by the non-linear mapping techniqueof Sammon (see International Publication No. WO 95/33848 and referencestherein). The number within each circle is indicative of the week inwhich the measurement was taken. FIG. 2 (patient #1) and 3 (patient #3)clearly show a dramatic shift in the response with time. In FIG. 2 theshift occurs between weeks #6 and #7; in FIG. 3 the shift is observedbetween weeks #7 and #8. The shifts in response profile correlate withthe healing process. FIG. 4, corresponding to patient #10, shows a largedifference in response at week #8, followed by a reversion to theprevious response type in week ten (week #2 appears to be an extraneousresult). This correlates with a clinical relapse in ulcer healingsuffered by patient #10.

The pathogenic organisms identified by the biopsies were found deep inthe ulcer base. These organisms are often not detected by routinesurface swabbing, but they are detectable by gas analysis with an arrayof gas sensors. Furthermore, and very importantly, the effectiveness ofany treatment may be assessed by monitoring the gases and vaporsproduced by these organisms. Thus by compiling a database of responseover the course of treatment, the effectiveness of any subsequenttreatment may be monitored.

Since the response patterns are dependent on numerous factors, it ispossible to correlate the observed responses with culturing conditionsand derive more detailed information about the detected bacteria.Therefore, it may be possible to deduce the location of the bacterialinfection from the response of the sensor array, or some other factor,such as pH.

EXAMPLE 2

Measurements were performed on the respiratory gases of two patientspresenting with pulmonary candida albicans, using the AromaScan A32Sinstrument. Measurements of response patterns were made once a day overa period of five days.

FIG. 6 shows the results in the form of a Sammon map, the rectangles 60representing measurements of the respiratory gases of a first patient,and the triangles 62 representing measurements of the respiratory gasesof a second patient. The data obtained from the second patient are quitestable, the response patterns being grouped in a tight cluster. Thiscorrelates with the fact that the patient was very stable, subsequentlymaking an excellent recovery. In contrast, the response patternsobtained from the first patient vary greatly from day to day. Thiscorrelates with the fact that the patient was very unstable, and died onthe fifth day of measurements.

The invention comprises, in another aspect, a method for identifying amicro-organism comprising the steps of:

providing at least one gas sensor 12;

compiling a database 14 of responses to at least one knownmicro-organism under a variety of culturing conditions;

abstracting gas and/or vapor from a detection region 16 and flowing thesame over the at least one gas sensor 12 and observing the response ofthe at least one sensor 12; and

comparing the responses to the database 14.

As described above, International Publication No. WO 95/33848 disclosesa method for identifying bacteria in which an array of gas sensorsanalyses gas or vapor produced by bacterial metabolism. The presentinvention recognizes that the method can be greatly improved byaccounting for variations in culturing conditions. Such conditionsinclude the nature of the nutrients (including the nature of anysubstrate involved in the culturing), the isolate employed, theincubation temperature and the stage in the bacterial life cycle atwhich measurements are made. It has been found that variation of theseconditions can cause variations in metabolic profiles, and hence in thecomposition of the characteristic gases or vapors released by bacterialactivity, thereby hampering or preventing the identification of thebacteria. In the present invention, variations in culturing conditionsare accounted for by compiling a database of responses of the sensorarray 10 to known bacteria under a variety of culturing conditions andrelating the pattern obtained with an “unknown” sample to this database.

The present invention also recognizes that other micro-organisms, suchas viruses, fungi or parasites may be identified by detecting gasesand/or vapors in this way.

The database preferably comprises responses to known micro-organismsunder a variety of nutrient conditions, since such variations cangreatly affect the composition of the gases or vapors emitted by thebacteria and detected in the present method. Thus, for example, theculturing media might comprise a modified broth, where one or moreamino-acids are missing, or complex matrices, such as a blood basedmedium versus a peptone medium.

Preferably, the database further comprises responses to a plurality ofdifferent isolates of a single bacterial species.

The database may comprise responses to known bacteria incubated at avariety of temperatures, since the above described composition of gasesor vapors is temperature dependent.

Additionally, the database may comprise responses to known bacteriaobtained at different stages in the bacterial life cycle. The responsesobtained from measurement of emitted gases or vapors vary as thebacteria pass through different metabolic phases, e.g. division(proliferation), growth arrest (product production/secretion), apoptosisor morbidity through starvation or external causation. Thus, it ispossible to derive from the array response, in addition to the identityof bacteria, more detailed information concerning the condition of thebacteria and to avoid a negative result from a known bacterium presentedin a condition not previously encountered, for which, however, thedatabase needs to be comprehensive. Furthermore, by measuring a cultureat a stage where only a few bacteria are present, and following thegrowth curve of the emitted gases and vapors, it is possible todiscriminate between bacterial cultures at a very early stage of growth.

In the present embodiment, a sensor array 10 comprising a plurality ofsemiconducting polymer gas sensors is employed. It is quite possiblethat arrays of other types of gas sensors, such as MOS, SAW and quartzresonator devices, or combinations thereof, are also suitable for thepresent purposes. The semiconducting polymer array 10 is a commerciallyavailable, twenty or thirty two sensor instrument (A20S and A32S,manufactured by AromaScan plc, Crewe, UK). Gas detection is accomplishedby measuring changes in de electrical resistance, these changes beingcaused by exposure of the sensors 12 to the gas. Thus, the response ofthe sensor array 10 is the pattern of resistance changes across thearray 10. Usually, the response is normalised in an appropriate manner.

Pattern recognition is achieved with a neural network 14, which shouldbe considered to be a “database” for the present purposes. The neuralnetwork 14 is trained to recognize the response pattern associated withknown bacteria under a variety of culturing conditions. Thereafter, whenan unknown response pattern is presented to the neural network 14,comparison with the response patterns used for training is made, via theoutputs of the nodes embedded within the neural network architecture.More information concerning suitable neural networks may be found inInternational Publication No. WO 96/26492 and references therein.

It should be noted that other sensor interrogation techniques other thanmeasurements of d.c. resistance are within the scope of the invention.

An important aspect of the present invention is the identification of atleast one condition in a patient in which gas and/or vapor produced bythe patient, or by a sample obtained from the patient, is flowed overthe at least one gas sensor(s). Examples of such samples are respiratorygases or swabbed samples, such as described in Example 1 and 2. In thisinstance, where a separate culturing step is not performed, it is notpossible to provide standardised growth conditions—the growth conditionsexperienced by the micro-organism in question are those provided invivo. Preferably, at least a portion of the database is compiled fromthe responses of the sensor(s) to gas and/or vapor produced by apatient, or a sample obtained from the patient. In other words, responsedata such as that presented in Examples 1 and 2 can be used to compilethe database. In this way, the database comprises responses to knownmicro-organisms obtained at different stages during the course oftreatment. Furthermore, the comparison of sensor response to thedatabase can be used to indicate the severity or progression of aninfection.

EXAMPLE 3

The importance of accounting for culturing conditions is illustrated bya study of six different species (E. Coli, Staph aureus, Coagulasenegative staphylococci, Group A Streptococcus and Proteus mirabilis)using the A32S instrument.

Ten different clinical isolates of the six bacterial species werecollected and cultured. Each specimen was cultured on the appropriatemedium and incubated at 37° C. for 24 hours.

Forty mls of sterile nutrient broth (Difco) in a sterile 250 ml Duranbottle were inoculated with each isolate and incubated at 37° C. for 24hours. Thereafter a 1:10 dilution of the broth was made, again in asterile Duran bottle and incubated for one hour.

The bottles were removed from incubation and the caps changed for capshaving inlet 18 and outlet 20 ports (see FIG. 1). Each bottle wasconnected to the A32S sensor array 10 for sampling, thereby providingsixty response patterns. These response patterns were used to train theneural network to recognize the six abovementioned bacterial species.

The system was then tested with ten isolates of each of the sixbacterial species (i.e. a total of sixty samples). The isolates employedwere different to the isolates used for training the neural network, andwere introduced to the system in a random order.

Of the sixty samples, fifty eight were recognized by the neural network(97% success rate). Two specimens were labelled “unknown”. In all fiftyeight cases of accurate recognition, the confidence of recognition layin the range 89 to 100%.

TABLE 1 Time to observe res- ponse Age/ change Patient Sex Biopsy Result(weeks) Comments 1 64/F S. aureus 6 See FIG. 2. Improvement started at3rd visit. Huge improvement at week 6. 2 74/M β-haemolytic strep 4Marked improvement week 3. 3 75/F β-haemolytic strep- 7 See FIG. 3.Steady Proteus improvement weeks 4-10. 4 67/M β-haemolytic strep/ 5 Hugeimprovement by Pseudomonas sp. week 5. 5 71/F No bacterial growth 2Improvement week 3. 6 74/M β-haemolytic strep/ 6 Remained healed. S.aureus Proteus sp. Slow improvement weeks 2-7. 7 81/M β-haemolyticstrep/ 4 Improvement by week S. aureus 4. 8 60/M β-haemolytic strep/ 10Improvement weeks S. aureus 6-14 9 71/F β-haemolytic strep 6 Slowimprovement weeks 4-8. 10 80/M β-haemolytic strep 7 See FIG. 4. Ulcergranulating weeks 3, followed by steady improvement until clinicalrelapse at week 9. 11 79/F No bacterial growth 2 Improvement weeks 3-5.12 55/F β-haemolytic strep 5 Steady improvement weeks 2-7. 13 69/Mβ-haemolytic strep/ 5 Rapid healing weeks Pseudomonas sp. 4-6. 14 52/MPeptostreptococcus sp. 4 Slow improvement weeks 3-9. 15 81/Fβ-haemoltyic strep/ 2 marked improvement Proteus sp. week 2.

What is claimed is:
 1. A method for monitoring at least one condition ina patient comprising the steps of: obtaining samples from the patientover a period of time; flowing the samples, or gases associated with orproduced by the samples, over at least one gas sensor; measuring theresponse or responses of the at least one gas sensor; and correlatingthe response or responses with the occurrence or state of the at leastone condition.
 2. The method according to claim 1 in which the samplescomprise respiratory gases.
 3. The method according to claim 1 in whichthe samples comprise swabbed samples obtained from the patient.
 4. Themethod according to claim 1 in which the samples comprise blood.
 5. Themethod according to claim 1 in which the condition monitored is adisease state.
 6. The method according to claim 5 in which theprogression and/or the regression of the disease state is monitored. 7.The method according to claim 5 in which the condition is a bacterialinfection.
 8. The method according to claim 5 in which the condition isa viral, fungal or parasitic infection.
 9. The method according to claim1 in which the response or responses are correlated with theeffectiveness of a course of treatment.
 10. The method according toclaim 1 in which the response or responses are correlated with progressof a healing process.
 11. The method according to claim 1 in which theresponse or responses are correlated with the occurrence or state of thecondition or conditions by a trained neural network.
 12. The methodaccording to claim 1 in which an array of gas sensors is employed. 13.The method according to claim 12 in which the pattern of responses ofthe sensors in the array is correlated with the occurrence of or stateof the at least one condition.
 14. The method according to claim 1 inwhich the samples are obtained continuously from the patient.
 15. Themethod according to claim 14 in which the samples, or gas associatedwith or produced by the samples, are continuously flowed over the atleast one gas sensor.
 16. The method according to claim 15 in which theresponse or responses of the at least one gas sensor is measuredcontinuously.
 17. The method according to claim 14 in which a pluralityof measurements are made over a period of time.
 18. The method accordingto claim 14 in which the samples comprise respiratory gases obtainedfrom a ventilator.
 19. The method according to claim 14 in which thesamples comprise blood undergoing a dialysis treatment.
 20. The methodaccording to claim 19 in which gases produced by a waste productcontaining solution are measured by the at least one gas sensor.
 21. Themethod according to claim 20 in which the removal of urea from the bloodsample is monitored by measuring ammonia evolved from the waste productcontaining solution.
 22. The method for identifying a micro-organismcomprising the steps of: providing at least one gas sensor; compiling adatabase of responses to at least one known micro-organism under avariety of culturing conditions; abstracting gas or vapor from adetection region and flowing the same over said the at least one gassensor and observing the response of the sensor or sensors; andcomparing the response to the database.
 23. The method according toclaim 22 in which the database further comprises responses to at leastone known bacterium.
 24. The method according to claim 22 in which thedatabase comprises responses to a plurality of different isolates of asingle bacterial species.
 25. The method according to claim 22 in whichthe micro-organism comprises a virus, fungus or parasite.
 26. The methodaccording to claim 22 in which the database comprises responses to atleast one known micro-organism cultured under a variety of nutrientconditions.
 27. The method according to claim 22 in which the databasecomprises responses to at least one known micro-organism cultured at avariety of temperatures.
 28. The method according to claim 22 in whichthe database comprises responses to at least one known micro-organismobtained at different stages in the life cycle of the micro-organism.29. The method for identifying at least one condition in a patientaccording to claim 22 in which gas and/or vapor produced by the patient,or by a sample obtained from the patient, is flowed over the at leastone gas sensor.
 30. The method according to claim 29 in which the atleast a portion of the database is compiled from the responses of atleast one gas sensor to gas and/or vapor produced by a patient, or by asample obtained from the patient.
 31. The method according to claim 22in which the database comprises responses to at least one knownmicro-organism obtained at different stages during the course oftreatment.
 32. The method according to claim 22 in which an array of gassensors is employed.
 33. The method according to claim 22 in which thecompilation of the database comprises training a neural network.
 34. Themethod according to claim 22 in which the response of the sensors isused to provide information about the detection region.
 35. The methodaccording to claim 22 in which the gas sensor or sensors comprise a gassensitive material.
 36. The method according to claim 35 in which anelectrical property of the gas sensitive material varies on exposure togases.
 37. The method according to claim 36 in which the gas sensitivematerial comprises semiconducting polymer.
 38. The method according toclaim 35 in which the gas sensor or sensors comprise MOS, quartzresonator or SAW devices.